Hierarchies relating Topology and Geometry
Walter Kropatsch, Yll Haxhimusa
Technische Universität Wien
Cognitive Vision has to represent, reason and learn about
objects in their environment it has to manipulate and react to.
There are deformable objects like humans which cannot be described
in simple geometric terms. In many cases they are composed of several
pieces forming a 'structured subset of Rn or Zn'.
We introduce the potential topological representations for structured
objects: plane graphs, combinatorial and generalized maps.
They capture abstract spatial relations derived from geometry and
enable reconstructions through attributing the relations
by e.g. coordinates. In addition they offer the possibility to combine both topology and geometry in a hierarchical framework:
irregular (graph) pyramids.
The basic operations to construct these hierarchies are edge contraction
and edge removal. We show results in using them
to hold a whole set of segmentations of an image
that enable reasoning and planning actions at various levels of detail
down to a single pixel in a homogeneous way.
We further speculate that the higher levels map the inherent structure
of objects and can be used to integrate (and 'learn') the specific object
properties over time by up-projecting individual measurements.
The construction of the hierarchies follows the philosophy to
reduce the data amount at each higher level of the hierarchy
by a factor > 1
while preserving important properties like connectivity and inclusion.